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\begin{document}

\title{Contribution of Climate Change and Urbanization to the Variation of
Extreme Precipitation in the Urban Agglomerations over the Loess Plateau}



\author[1]{Na Wei}%
\author[1]{Ninglian Wang}%
\author[2]{Yu rong  zheng}%
\affil[1]{Northwest University}%
\affil[2]{North-West University}%


\vspace{-1em}



  
  \date{May 25, 2024}


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\selectlanguage{english}
\begin{abstract}
With global warming and rapid urbanization, urban agglomerations over
the Loess Plateau (LP) are suffering from various urban disasters.
Urbanization has aggravated the decreasing trends of extreme
precipitation in Taiyuan and Xi'an urban agglomerations (UAs) and
enhanced the increasing trends of extreme precipitation in Luoyang,
Hohhot and Xining UAs during 1979--2018. Meanwhile, the number of light
rain days decreases in almost all the cities, indicating the sensitivity
of light rain days to urbanization. The climate change is a primary
contributor to the change of urban precipitation during 1980--2000.
However, the urbanization contribution has been increasing gradually
since 2000, and the urbanization further amplifies the trend of extreme
precipitation caused by the climate change. In terms of the physical
mechanisms, the rapid increasing surface temperature and aerosol
particles are closely related to the urban precipitation. Our findings
provide a systematic understanding of the urbanization effects on the
extreme precipitation over the LP and may play an important role in the
mitigation of urban disasters.%
\end{abstract}%



\sloppy


Contribution of Climate Change and Urbanization to the Variation of
Extreme Precipitation in the Urban Agglomerations over the Loess Plateau

Na Wei\textsuperscript{1,2}, NinglianWang\textsuperscript{1*}, Yurong
Zheng\textsuperscript{1}

\textsuperscript{1}Shaanxi Key Laboratory of Earth Surface System and
Environmental Carrying Capacity, Northwest University, Xi'an, Shaanxi
Province, China, 710027

\textsuperscript{2} Climate Centre of Shaanxi Province, Xi'an, China,
710049

\textbf{Abstract:} With global warming and rapid urbanization, urban
agglomerations over the Loess Plateau (LP) are suffering from various
urban disasters. Urbanization has aggravated the decreasing trends of
extreme precipitation in Taiyuan and Xi'an urban agglomerations (UAs)
and enhanced the increasing trends of extreme precipitation in Luoyang,
Hohhot and Xining UAs during 1979--2018. Meanwhile, the number of light
rain days decreases in almost all the cities, indicating the sensitivity
of light rain days to urbanization. The climate change is a primary
contributor to the change of urban precipitation during 1980--2000.
However, the urbanization contribution has been increasing gradually
since 2000, and the urbanization further amplifies the trend of extreme
precipitation caused by the climate change. In terms of the physical
mechanisms, the rapid increasing surface temperature and aerosol
particles are closely related to the urban precipitation. Our findings
provide a systematic understanding of the urbanization effects on the
extreme precipitation over the LP and may play an important role in the
mitigation of urban disasters.

Key words: Urban Extreme Precipitation Climate Change Urban Heat Island

\textbf{Introduction}

Global warming and urbanization are both rapidly changing the urban
climate environment, causing a series of urban climate problems, such as
urban heat island (UHI), urban waterlogging and water shortage, and air
pollution (Della-Marta, Haylock et al. 2007, Han, Baik et al. 2014, Ma,
Li et al. 2018, Paul, Ghosh et al. 2018). The UHI has been a common
phenomenon resulted from the urbanization, but it demonstrates different
impacts on the precipitation under different climatic backgrounds (Wang,
Zhou et al. 2007, Daniel, Lemonsu et al. 2018, Ma, Li et al. 2018, Yan,
Chan et al. 2020, Yang 2020)\{Chrysanthou, 2014 \#30;Mazdiyasni, 2015
\#23\}. Many studies revealed that the UHI can induce updrafts and
trigger moist convections, resulting in more frequent extreme
precipitation near the urban areas, such as Beijing and the Pearl River
Delta, than in the rural areas (Della-Marta, Haylock et al. 2007,
Drobinski, Silva et al. 2016). In the urban agglomerations (UAs) of the
Yangtze River Delta in East China, there is a prominent urban dry island
(UDI) effect, which is characterized by reduced humidity and increased
vapor pressure deficit in the urban core area (Du, Wang et al. 2019, Luo
and Lau 2019). Some researchers concluded that the urbanization tends to
augment the precipitation trends caused by the climate change at
national scale in China (Gu, Zhang et al. 2019). Moreover, the UHI
effects are enhanced more obviously in wet climates than in dry climates
(Zhao, Lee et al. 2014). It can be found that the urbanization impact on
temperature and precipitation may also be different under different
climatic backgrounds and terrain conditions (Liao, Liu et al. 2018).
Climate change is a dominant contributor for the urban precipitation
trends, but the urbanization has been exhibiting a relatively strong
influence since the reform and opening up in China. Assessing the
contributions of climate change and urbanization to the changes in urban
precipitation is important for the disaster risk management.

Previous studies mainly evaluated the urbanization effect by analyzing
the difference between urban and rural meteorological observation
stations (Zhao and Wu 2017, Luo and Lau 2018, Ma, Li et al. 2018).
Because of the urban expansion, the distance between urban and rural
stations and the number of urban and rural stations vary greatly, which
may cause uncertainty of the urbanization effect evaluation (Song, Zhang
et al. 2014). High-resolution gridded precipitation data and artificial
impervious area have been well applied in urban precipitation researches
(Su, Li et al. 2019). Most of these studies focused on individual UAs
such as Beijing, Shanghai and the Pearl River Delta. There are also a
few studies that give an overall landscape of the urbanization impact on
climate at the national scale in China (Gu, Zhang et al. 2019, Lin, Gao
et al. 2020). Therefore, it is very interesting to study the variation
characteristics of the precipitation in the UAs within a unique climate
zone.

The Loess Plateau (LP), located over the central-northern part of China
and covering seven provinces and the middle reaches of the Yellow River,
belongs to the wet-arid transition area and hence is sensitive to
climate change. In recent decades, it has suffered severe water and soil
losses and ecological environment changes, with decreasing trends of
precipitation extreme in the Fen-Wei River valley and increasing trends
of rainfall intensity and frequency in the northwestern LP, which exert
negative effects on the social and economic developments (Zhao, Li et
al. 2017, Zhang, Gao et al. 2020). Meanwhile, the LP has experienced
drastically rapid urbanization in the last 20 years, and six typical UAs
(Taiyuan, Xi'an, Hohhot, Yinchuan, Xining-Lanzhou and Luoyang) home to
70 percent of the LP population play an important role in the economic
development and the ecological security(MA BB 2019). Precipitation
over the LP is primarily affected by the complex terrain, the
land-atmosphere interaction and the monsoon. During July to September,
the East Asian summer monsoon has a dominant effect on the LP at a
spatial scale far beyond the urban areas, which may overwhelm the local
urban impacts. However, during April--June, the rainfall is much weaker
and thus local urban effects may be more evident.

In this study, we present the first analysis of the changes of urban
extreme precipitation over the LP in different climate zones based on a
set of high-resolution gridded precipitation data. We also evaluate the
contributions of climate change and urbanization to the urban
precipitation across several UAs over the LP during different periods by
using an effective attribution method.

The paper is structured as follows. Section 2 introduces the study area,
the data, the definition of extreme climate events and the methods used
in the study. Section 3 presents and discusses the analysis results.
Conclusions are made in Section 4.

\textbf{2. Data and Methods}

\textbf{2.1. Study area}

The LP covers 62.4\selectlanguage{ngerman}×104 km\textsuperscript{2} (34--45°5'N, 101--114°33'N)
and is located in the upper and middle reaches of the Yellow River in
the central-northern part of China. The LP is surrounded by complicated
mountain conditions, with the Yin Mountain in the north, the Qinling
Mountain in the south, the Riyue Mountain in the west and the Taihang
Mountain in the east. The terrain over the LP gradually declines from
northwest to southeast, with a unique topography composed of abysses,
ridges, gullies and hills. The LP lies in the continental monsoon
climate zone, spanning 750 km from north to south and 1000 km from east
to west, so the LP exhibits various climate characteristics (Zhang, Wu
et al. 2014, Liu, Wang et al. 2016, Zhao, Zhai et al. 2017). The LP
covers a wide temperature gradient decreasing from south to north and a
large precipitation gradient decreasing from southeast to northwest.

\textbf{2.2. Data}

\textbf{2.2.1. China meteorological forcing dataset}

The China Meteorological Forcing Dataset (CMFD) is a gridded
near-surface meteorological dataset with high spatio-temporal
resolution, which was developed by the hydrometeorological research
group of the Institute of Tibetan Plateau Research of the Chinese
Academy of Sciences. (Yang, He et al. 2010, Yingying, Chen et al. 2011).
The dataset was obtained through fusion of remote sensing products,
reanalysis dataset and in-situ observation data at weather stations. Its
record starts from January 1979 and keeps extending (currently up to
December 2018) with a temporal resolution of three hours and a spatial
resolution of 0.1°. Seven near-surface meteorological elements are
provided in the CMFD, including 2-m air temperature, surface pressure,
specific humidity, 10-m wind speed, downward shortwave radiation,
downward longwave radiation and precipitation rate.

\textbf{2.2.2. Global artificial impervious areas}

Artificial impervious areas are predominant indicators of human
settlements. Timely, accurate and frequent information on the artificial
impervious areas is critical for understanding the urbanization process
and the land use/cover change, as well as their impacts on the
environment and the biodiversity. Based on the full archive of the 30-m
resolution Landsat images on the Google Earth Engine platform, the
annual global artificial impervious areas (GAIA) are mapped from 1985 to
2018 (Gong, Li et al. 2020). With the ancillary datasets, including the
nighttime light data and the Sentinel-1 Synthetic Aperture Radar data,
the performance of our previously developed algorithm is improved in
arid areas. The GAIA dataset can be freely downloaded from
http://data.ess.tsinghua.edu.cn.

The GAIA data with a spatial resolution of 30 m from 2000 to 2018 are
used in this study to investigate the urbanization development over the
LP during this period (He, Yang et al. 2020). According to the GAIA data
in 2018 and 2000, there are six UAs over the LP, including Xi'an,
Taiyuan, Luoyang, Hohhot, Yinchuan and Xining-Lanzhou (Fig. 1). The
artificial impervious area in Xi'an, Taiyuan, Luoyang and Hohhot reached
1400 km\textsuperscript{2},1700 km\textsuperscript{2}, 1000
km\textsuperscript{2} and 1000 km\textsuperscript{2} in 2018, which is 2
times, 2 times,1.5 times and 1.5 timesthat in 2000, respectively (Fig.
2). Due to the relatively slow economic development, the artificial
impervious area in Yinchuan, Xining and Lanzhou only increased by
100--200 km\textsuperscript{2} in 2018 compared to 2000.

\textbf{2.2.3. PM\textsubscript{2.5} hindcast database}

The PM\textsubscript{2.5} hindcast database (PHD) provides the
PM\textsubscript{2.5} estimation across China from 2000 to 2017. By
using a machine learning approach, the PHD assembles datasets from
multiple sources, including the moderate-resolution imaging
spectroradiometer satellite measurements of aerosol, the Community
Multiscale Air Quality model outputs based on the historical
multi-resolution emission inventory for China and many other variables.
It hindcasts the daily PM\textsubscript{2.5} concentrations from 2000 to
2017 in China. The PHD is developed and maintained by a team from
Tsinghua University, Beijing, China (Xue, Zheng et al. 2019).

\textbf{2.3. Methods}

\textbf{2.3.1. Trend detection}

We analyze and discuss the extreme precipitation over the LP during the
period from 1979 to 2018 by using the Mann-Kendall test and the Sen's
slope estimator (Hamed 1998). The Mann-Kendall test is used to detect
the significance of the change trend at each grid point. In this study,
there are 13,500 grid points over the LP in the CMFD to be tested. At
the 0.05 significance level, it will yield 675 (13,500 × 0.05) grid
points where the significance cannot be determined, which is too high to
be acceptable. Benjamini and Hochberg (1995) proposed the false
discovery rate (FDR) procedure to apply the multiple Mann-Kendall tests
to adjust the signi\selectlanguage{english}ficance level and control the proportion of falsely
rejected null hypotheses relative to the total number of rejected
hypotheses.

The Sen's slope is applied to estimate the linear trend during 1979 to
2018 for each grid point (Sen and Kumar 1968). A positive slope value
indicates an increasing or upward trend in the time series, while a
negative value reveals a decreasing or downward trend. Similarly, zero
indicates no trend.

\textbf{2.3.2. Estimation of urbanization and climate change
contribution}

In this study, we evaluate the contribution of climate change and
urbanization based on the ``trajectory'' method (Feng, H. et al. 2014,
Feng and Huihui 2016). At local scales, climate change and urbanization
jointly influence the precipitation trends at some certain grids. Their
potential contributions at a certain UA can be calculated as follows.

UE = R\textsubscript{u}-R\textsubscript{r} , (1)

UC =S\textsubscript{sub} x(R\textsubscript{u}
-R\textsubscript{r})/R\textsubscript{u}x100\% , (2)

CC = (R\textsubscript{r}/R\textsubscript{u})x100\% , (3)

where R\textsubscript{u} and R\textsubscript{r} are the precipitation
trends in urban and rural areas, respectively. The urbanization effect
(UE) is defined as the trend difference between the urban and the rural
series in Eq. 1. S\textsubscript{sub} is the proportion of urban grids
to total grids. If the rural and the urban grids have the same number in
the contribution analysis at regional scale, the parameter
S\textsubscript{sub} can still be remained for the sake of equation
generality. The relative urbanization contribution (UC) in percentage is
defined as the fraction of the overall trends of the extreme
precipitation events attributed to the UE in Eq. 2. The relative climate
change contribution (CC) is defined as the ratio of the precipitation
trend in the rural areas to that in the urban areas in Eq. 3 (Gu, Zhang
et al. 2019).

\textbf{2.3.4. Extreme precipitation index}

We adopt the precipitation data from April 1 to July 1 in this study,
excluding the persistent heavy precipitation caused by summer monsoon.
Four extreme precipitation indices are selected to represent the
intensity of presummer precipitation over the LP based on the gridded
data in the CMFD. The four indices include the maximum daily
precipitation (R\textsubscript{max}), the very wet day precipitation
(R\textsubscript{95p}) which is the total precipitation when daily
precipitation \textgreater{} 95\textsuperscript{th} percentile mean
daily precipitation, the extremely heavy precipitation
(R\textsubscript{\textgreater{}10mm}) which is the total precipitation
when daily precipitation \textgreater{}10 mm and the number of light
rain days (R\textsubscript{\textless{}10mm}) which is the count of days
with daily precipitation \textless{} 10 mm during the presummer season.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\par\null

FIG. 1. Terrain height and urban agglomerations over the Loess Plateau.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

FIG. 2. Spatial distribution (a) and temporal variation (b) of the urban
agglomeration area in 2000 and 2018 over the Loess Plateau.

\textbf{3. Results and discussion}

\textbf{3.1. Spatial distributions of presummer precipitation trends}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

FIG. 3. Spatial distributions of the linear trends of (a)
R\textsubscript{max}, (b) R\textsubscript{\textgreater{}10mm}, (c)
R\textsubscript{95p} and (d) R\textsubscript{\textless{}10mm} over the
Loess Plateau during the presummer season from 1979 to 2018.

The FDR-adjusted Mann-Kendall test is applied to detect the significance
of the trend of the four presummer precipitation indices during
1979--2018 at each grid point over the LP. The black dots indicate the
grid points with the trend passing the significance test at 90\%
confidence level. In general, the extreme precipitation increases in the
northwest and decreases in the southeast of the LP (Fig. 3), which is
basically consistent with the precvious studies (Zhao, Zhai et al. 2017,
Zhao, Li et al. 2017). However, significant differences are found among
the six UAs. Xi'an, Taiyuan and Luoyang are located in the Fenhe and
Weihe River Basin in the southeastern LP, with the fastest urbanization
development over the LP (Fig. 2). Under the climate background with
decreasing precipitation, the extreme precipitation decreases more
severely in Taiyuan UA and the rural areas. The rates of
R\textsubscript{max}, R\textsubscript{\textgreater{}10mm} and
R\textsubscript{95p} in Taiyuan UA are 0.2 mm\selectlanguage{ngerman}·year \selectlanguage{english}\textsuperscript{-1},
0.4\selectlanguage{english}-0.6 mm*year\selectlanguage{english}\textsuperscript{-1} and 0.4\selectlanguage{english}-0.5 mm*year
\selectlanguage{english}\textsuperscript{-1}, respectively, which are lower than those in the
rural areas. The most significant effect of urbanization is found in
Taiyuan UA (Fig. 3c), where the decrease rates of R\textsubscript{95p}
in the urban and the rural areas presents clearly different
distributions. In obvious contrast with Taiyuan, Luoyang is under a
climate background with increasing precipitation, and an obvious center
with increasing extreme precipitation is found in Luoyang UA. The
increase rates of R\textsubscript{max},
R\textsubscript{\textgreater{}10mm} and R\textsubscript{95p} in Luoyang
UA are 0.3 mm*year \selectlanguage{english}\textsuperscript{-1}, 0.7 mm*year
\selectlanguage{english}\textsuperscript{-1} and 0.9 mm*year \selectlanguage{english}\textsuperscript{-1}, respectively,
which are higher than those in its rural region. Xi'an is different from
the two above UAs. Xi'an UA is in a wet environment, but a dry core is
formed in the UA center due to the urbanization effect, which is
obviously reflected in the distribution of R\textsubscript{max} and
R\textsubscript{95p} (Figs. 3a and 3c). The extreme precipitation rate
decreases by 0.4\selectlanguage{english}-0.6 mm*year\selectlanguage{english}\textsuperscript{-1} in the center of Xi'an
UA compared with that in the rural areas. Hohhot and Xining are two UAs
in the northern and western parts of the LP, respectively, where the
precipitation has increased significantly in recent years. In the past
two decades, the urbanization of Xining has been relatively slow, but it
is particularly sensitive to the urbanization as a plateau city. The
extreme precipitation in Xining UA is more concentrated in the center,
especially for R\textsubscript{max} and
R\textsubscript{\textgreater{}10mm}. Hohhot has witnessed rapid
urbanization in recent years, and its extreme precipitation
characteristics are similar to those of Xining. The trend distribution
of the number of light rain days is one of the most sensitive indicators
for the urbanization effect. Different from the trend of extreme
precipitation, the number of light rain days in the five UAs
consistently decreases except for Luoyang, which corresponds~to the
change in the artificial impervious area (Figs. 2a and 4d).

The trends of extreme precipitation in Taiyuan, Luoyang, Hohhot and
Xining UAs almost pass the significance test at 90\% confidence level,
while that in Xi'an does not pass the significance test, with opposite
trend in the urban and the rural areas. The above analysis shows that
the urbanization plays an accelerator role in the urban climate
evolution, and the urban precipitation has become more intense and
sensitive than that in the rural areas.

\textbf{3.2. Trends variation of presummer precipitation in UAs}

In order to further investigate the effect and contribution of climate
change and urbanization on the presummer precipitation in different
periods, we divide the five UAs into urban and rural areas separately
according to the map of artificial impervious area cover. The UA area is
covered by the artificial impermeable area, and the rural areas are
mapped by using a buffer method. A buffer zone of 20 kilometers around
the urban area is defined as the rural buffers (Fig. 1). The
precipitation differences between the urban and the rural areas are
calculated and compared in this section. The 30-m resolution land cover
map is regridded into a 0.1deg GAIA map according to the spatial
resolution of the CMFD dataset. For each grid of the new GAIA map, if
the area of the original urban grids accounts for over 50\% of its total
area, it is defined as an urban grid; otherwise, it is defined as a
rural grid. The precipitation indices are then spatially averaged over
the urban and the rural grids separately, and the trend of each index is
calculated based on the spatial mean during 1979--2018.

The analysis of section 3.1 reveals that the urbanization plays a role
in amplifying the local climate change. Although the climate change is
the main contributor to the urban precipitation trend, the effect of
urbanization has been gradually identified in recent years. We apply the
Student's t-test to detect the change point of the precipitation over
the LP and divide the study periods into two stages. Most of the extreme
precipitation indices in the five UAs demonstrate a sudden change point
in the middle or late 1990s (Table 1). This is in accord with the
previous studies indicating an obvious inter-decadal variation of the
precipitation over the LP, with the significant turning point in the
middle and late 1990s. During this period, the UAs had been growing more
rapidly. Therefore, it is inferred that the climate change plays a
primary role in the first stage of the precipitation series, and the
urbanization effect gradually emerges in the second stage.

Figures 4 to 7 and Table 1 display the evolution of
R\textsubscript{max}, R\textsubscript{10mm}, R\textsubscript{95p} and
the light rain days in the five UAs and their rural areas. In the first
stage, the extreme precipitation exhibits consistent trends in the urban
and the rural areas of the five UAs (Fig. 4 and Table 1). However, the
discrepancies between the urban and the rural widen in the second stage.
The R\textsubscript{max}, R\textsubscript{10mm} and R\textsubscript{95p}
keep growing slowly in both the urban and the rural areas of Xi'an
before 2001, however, the trends of extreme precipitation become
discrepant between the urban and the rural areas after 2001, with
increase in the urban areas and decrease in the rural areas. The
R\textsubscript{max}, R\textsubscript{10mm} and R\textsubscript{95p} in
the urban and the rural areas of Taiyuan maintain a decreasing trend,
which becomes steeper in the urban areas than in the rural areas in the
second stage. For example, the trends of R\textsubscript{95p} in the
urban and the rural areas of Taiyuan after 2001 are \selectlanguage{english}-1.09 mm*year
\selectlanguage{english}\textsuperscript{-1}and \selectlanguage{english}-0.06 mm*year \selectlanguage{english}\textsuperscript{-1}, respectively
(Table 2). The R\textsubscript{max}, R\textsubscript{10mm} and
R\textsubscript{95p} in Xining, Hohhot and Luoyang show a consistent
increase trend in both the urban and the rural areas. In the second
stage, the increase rates in the urban areas get greater than that in
the rural areas, all of which are significant at a 0.10 significance
level (Table 2). The linear trends of the number of light rain days in
the five UAs show similar characteristics in the two stages, but the
variation is significant. In the second stage, the number of light rain
days in the rural areas of Hohhot, Taiyuan and Xining increases more
significantly than that in the urban areas, on the contrary, that in the
urban areas of Xi'an decreases much more than that in the rural areas.
As a result, the number of light rain days increases less in the urban
areas than in the rural areas. The above analyses illustrate that the
climate change is the dominant factor for the urban precipitation in the
first stage, but the urbanization effect is further revealed in the
second stage.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}

\par\null

FIG. 4. Trend variation of presummer R\textsubscript{max} in the urban
(red lines) and the rural (black lines) areas of the five UAs over the
Loess Plateau during 1979--2018.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image9/image9}

\end{center}
\end{figure}

FIG. 5. The same as Fig. 4, but for R\textsubscript{10mm.}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image10/image10}

\end{center}
\end{figure}

FIG. 6. The same as Fig. 4, but for R\textsubscript{95p.}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image11/image11}

\end{center}
\end{figure}

FIG. 7. The same as Fig. 4, but for light rain days.

To further quantify the contribution of climate change and urbanization
to the precipitation, Table 2 shows the changes of the relative
contributions in the two stages in the five UAs of the LP. Spatially,
the urbanization tends to exert negative impacts on the extreme
precipitation indices in the basin areas (such as Taiyuan and Xi'an)
except for Luoyang in the southeastern LP, while positive urbanization
effects are mainly found in the UAs located in the north and the west of
the LP (including Hohhot and Xining). It is also found that the effect
and contribution of urbanization are not only dependent on the study
period, but also strongly correlated to the trend of th extreme
precipitation index. In the first stage, the effect and contribution of
urbanization on the urban precipitation are relatively weak, and the
climate change is the dominant factor. In the second stage, the effect
and contribution of urbanization have increased dramatically in most
UAs, while the contribution of climate change is much weaker than that
in the first stage. Among these extreme precipitation indices, the
R\textsubscript{10mm} and the R\textsubscript{95p} in Luoyang and the
R\textsubscript{10mm} in Hohhot demonstrate little differences of urban
effect between the two stages, indicating that the urbanization does not
always have a strong effect on all the extreme precipitation indices. In
addition, the urbanization tends to augment the trends of precipitation
caused by the climate change in all the five UAs. (For example, the
urban areas of Taiyuan and Xi'an have become drier and Hohhot and Xining
become wetter than their surrounding rural areas. The wetting trend in
northwestern LP and the drying trend in southeastern LP were only
attributed to the climate change in the previous researches. The results
in this study demonstrate that the urbanization also plays an important
role in the extreme precipitation change in the recent two decades.

TABLE 1. Mean trend of presummer precipitation from 1979 to 2018.\selectlanguage{english}
\begin{longtable}[]{@{}lllllllllllll@{}}
\toprule
city & \textbf{R\textsubscript{max}} & \textbf{R\textsubscript{max}} &
\textbf{R\textsubscript{max}} & \textbf{R\textsubscript{10mm}} &
\textbf{R\textsubscript{10mm}} & \textbf{R\textsubscript{10mm}} &
\textbf{R\textsubscript{95p}} & \textbf{R\textsubscript{95p}} &
\textbf{R\textsubscript{95p}} & \textbf{Light Rain} & \textbf{Light
Rain} & \textbf{Light Rain}\tabularnewline
\midrule
\endhead
& mm/day/year & mm/day/year & mm/day/year & mm/year & mm/year & mm/year
& mm/year & mm/year & mm/year & days/year & days/year &
days/year\tabularnewline
& Change point & urban & rural & Change point & urban & rural & Change
point & urban & rural & Change point & urban & rural\tabularnewline
\textbf{Xi'an} & 2003 & & & 2000 & & & 2001 & & & 1993 &
&\tabularnewline
& 1979-2003 & 0.04 & 0.06 & 1979-2000 & 0.4 & 0.9* & 1979-2001 & 0.7* &
0.34 & 1979-1993 & 0.12 & 0.20\tabularnewline
& 2004-2018 & -0.3* & 0.38* & 2001-2018 & -0.21 & 0.99* & 2002-2018 &
-2.4* & 1.67* & 1994-2018 & 0.14 & 0.22\tabularnewline
\textbf{Xining} & 1998 & & & 2000 & & & 1994 & & & 1999 &
&\tabularnewline
& 1979-1998 & 0.33 & 0.29 & 1979-2000 & 0.8* & 0.28 & 1979-1994 & 1.53*
& 3.98* & 1979-1999 & 0.31* & 0.29*\tabularnewline
& 1999-2018 & 0.40\textsuperscript{*} & 0.19 & 2001-2018 & 0.36 & -0.67*
& 1995-2018 & 1.79* & 0.15 & 2000-2018 & -0.08 & -0.02\tabularnewline
\textbf{Hohhot} & 2003 & & & 2002 & & & 1994 & & & 1998 &
&\tabularnewline
& 1979-2003 & 0.34 & 0.24 & 1979-2001 & 0.65* & 0.29 & 1979-1994 & 1.43*
& 1.52* & 1979-1998 & 0.01 & 0.10\tabularnewline
& 2004-2018 & 1.35\textsuperscript{*} & 0.95* & 2003-2018 & 0.85* &
0.67* & 1995-2018 & 0.54* & -0.79* & 1999-2018 & 0.10 &
0.25*\tabularnewline
\textbf{Taiyuan} & 1995 & & & 1997 & & & 2001 & & & 1993 &
&\tabularnewline
& 1979-1995 & -0.71\textsuperscript{*} & -0.62* & 1979-1997 & -0.01 &
-1.16* & 1979-2001 & -2.77* & -1.16* & 1979-1993 & 0.28* &
0.22*\tabularnewline
& 1996-2018 & -0.12 & 0.08 & 1998-2018 & -0.05 & 0.54* & 2002-2018 &
-1.09* & -0.06 & 1994-2018 & 0.11 & 0.34*\tabularnewline
\textbf{Luoyang} & 1996 & & & 1999 & & & 1994 & & & 1994 &
&\tabularnewline
& 1979-1996 & 0.90\textsuperscript{*} & 0.63* & 1979-1999 & 1.5* & -0.08
& 1979-1994 & 4.67* & 1.54* & 1979-1994 & -0.13 & 0.18\tabularnewline
& 1997-2018 & 0.17 & -0.06 & 2000-2018 & 3.34* & 1.75* & 1995-2018 &
1.96* & 0.86* & 1995-2018 & 0.15 & 0.08\tabularnewline
\bottomrule
\end{longtable}

*The trend is significant at the 0.10 significance.

TABLE 2. Mean trends of urbanization effect (unit: per year), relative
urbanization and climate change contribution (unit: %) for the five UAs
in the two stages.\selectlanguage{english}
\begin{longtable}[]{@{}llllllllllllll@{}}
\toprule
\textbf{City} & \textbf{Stage} & \textbf{R\textsubscript{max}} &
\textbf{R\textsubscript{max}} & \textbf{R\textsubscript{max}} &
\textbf{R\textsubscript{10mm}} & \textbf{R\textsubscript{10mm}} &
\textbf{R\textsubscript{10mm}} & \textbf{R\textsubscript{95p}} &
\textbf{R\textsubscript{95p}} & \textbf{R\textsubscript{95p}} &
\textbf{Light Rain} & \textbf{Light Rain} & \textbf{Light
Rain}\tabularnewline
\midrule
\endhead
& & \textbf{UE} & \textbf{UC} & \textbf{CC} & \textbf{UE} & \textbf{UC}
& \textbf{CC} & \textbf{UE} & \textbf{UC} & \textbf{CC} & \textbf{UE} &
\textbf{UC} & \textbf{CC}\tabularnewline
Xi'an & I & -0.02 & -50.0 & 100 & -0.52 & -49.5 & 100 & 0.25 & 35.7 &
100 & -0.08 & -66.7 & 100\tabularnewline
& \textbf{II} & \textbf{-0.71} & \textbf{-92.5} & \textbf{-100} &
\textbf{-1.21} & \textbf{-81.8} & \textbf{-100} & \textbf{-4.07} &
\textbf{-58.9} & \textbf{-100} & \textbf{-0.07} & \textbf{-57.2} &
\textbf{100}\tabularnewline
Xining & I & 0.04 & 12.1 & 87.8 & 0.51 & 62.5 & 35.0 & -2.31 & -42.8 &
100 & 0.02 & 6.4 & 93.5\tabularnewline
& \textbf{II} & \textbf{0.20} & \textbf{50.0} & \textbf{42.5} &
\textbf{1.01} & \textbf{95.6} & \textbf{-100} & \textbf{1.52} &
\textbf{84.9} & \textbf{8.3} & \textbf{-0.06} & \textbf{-75.0} &
\textbf{25}\tabularnewline
Hohhot & I & 0.10 & 29.4 & 70.5 & 0.37 & 56.9 & 44.4 & -0.08 & -5.6 &
100 & 0.09 & -90.0 & 100\tabularnewline
& \textbf{II} & \textbf{0.42} & \textbf{31.1} & \textbf{70.3} &
\textbf{0.14} & \textbf{17.8} & \textbf{78.8} & \textbf{1.33} &
\textbf{41.3} & \textbf{-100} & \textbf{-0.15} & \textbf{-95.5} &
\textbf{100}\tabularnewline
Taiyuan & I & -0.08 & -12.7 & 87.3 & -1.1 & -10.2 & 100 & -1.42 & 52.5 &
41.8 & -0.06 & -21.4 & 78.5\tabularnewline
& II & \textbf{-0.21} & \textbf{-57.1} & \textbf{-66.7} & \textbf{-0.6}
& \textbf{-90.0} & \textbf{-100} & \textbf{-1.01} & \textbf{92.6} &
\textbf{5.5} & \textbf{-0.23} & \textbf{-47.8} &
\textbf{100}\tabularnewline
Luoyang & I & 0.31 & 33.3 & 70.0 & 1.57 & 95 & -5.3 & 3.12 & 66.8 & 32.9
& -0.31 & -41.9 & -100\tabularnewline
& II & \textbf{0.23} & \textbf{73.2} & \textbf{35.2} & \textbf{1.63} &
\textbf{48.8} & \textbf{52.3} & \textbf{1.11} & \textbf{56.6} &
\textbf{43.8} & \textbf{0.07} & \textbf{46.7} &
\textbf{53.3}\tabularnewline
\bottomrule
\end{longtable}

\textbf{3.3. Urbanization effect on the presummer precipitation over the
Loess Plateau}

The previous analyses suggested that the changes in large-scale
atmospheric circulations are likely the main cause of the variation in
extreme precipitation patterns. However, the significant discrepancies
between the trends in the urban and the rural areas indicate a
remarkable effect of urbanization on the variability of extreme
precipitation over the LP, especially in the last 20 years. The
urbanization causes many climatic problems, such as the increase of
urban surface temperature, the decrease of wind speed and the increase
of aerosols. However, the physical process of the urbanization effect on
the extreme precipitation is quite complicated. In this section, we only
preliminarily discuss the possible effects of temperature and aerosols
on the urban precipitation over the LP.

The UHI effect may be one factor influencing the presummer precipitation
over the LP. Except for the Xining UA with unsignificant increase of
surface air temperature, all the other UAs exhibit significant UHI
characteristics, with the mean trend of surface temperature in the urban
areas 0.05--0.07\selectlanguage{ngerman}°C·year\selectlanguage{english}\textsuperscript{-1} higher than that in the
rural areas during 1979--2018 (Fig. 8). The previous studies showed that
urbanization has different impacts on urban precipitation in wet and dry
climate backgrounds (Luo and Lau 2019, Yang 2020). The warmer surface in
the urban core areas induces stronger potential evapotranspiration by
exponentially increasing the saturated water vapor pressure and
aggravating the water vapor deficit. If the atmospheric water is scarce,
the urbanization further intensifies the drying trend over the urban
core areas compared with the surrounding rural areas, such as Xi'an and
Taiyuan. If the atmospheric water is abundant, the heat source in the
urban areas induces vertical flows and enhances convective motions,
leading to more convective precipitation in the urban areas than in the
rural areas, such as Luoyang and Hohhot (Yan, Chan et al. 2020).

Another major feature is the increasing aerosol emissions associated
with urbanization. In this study, PM\textsubscript{2.5} is taken as a
representative to reflect the aerosol distribution in the UAs over the
LP (Fig. 9). Many studies have indicated that aerosols may inhibit or
promote the precipitation with different intensity (Qian Duan 2008, Ma,
Huo et al. 2017, Fults, Massmann et al. 2019, Berhane and Bu 2021). For
instance, aerosols have obvious inhibitory effect on the light rain in
general. With sufficient water vapor, more aerosol particles will lead
more condensation nuclei to be formed (aerosol microphysical effect),
which further increases the extreme precipitation combined with the UHI
effect. The urban precipitation characteristics in Luoyang, Hohhot and
Xining UAs can be explained in this way. However, in the long run, the
increase of aerosol is not conducive to the precipitation formation.
Aerosols weaken the radiation reaching the ground, leading to a stable
low-level atmosphere, which is unfavorable for the ascending motion and
thus reduces the occurrence frequency of precipitation. When the water
vapor transport weakens, the increase of aerosol makes the atmospheric
stratification more stable, resulting in less precipitation frequency.
Xi'an and Taiyuan UAs have experienced frequent serious air pollution
incidents in recent years due to the impact of basin topography and
urbanization. The significant decrease of precipitation in the two UAs
should be closely related to the increase of aerosol in recent years.
Therefore, the urbanization influence on precipitation is actually the
result of the competition among the thermal forcing of UHI, the aerosol
radiation effect and the aerosol microphysical effect.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image12/image12}

\end{center}
\end{figure}

\par\null

FIG. 8. Spatial distribution of the linear trends of mean temperature
over the Loess Plateau during the presummer season from 1979 to 2018.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image13/image13}

\end{center}
\end{figure}

FIG. 9. Spatial distribution of the monthly mean PM2.5 over the Loess
Plateau during the presummer season from 2000 to 2017.

\textbf{4. Conclusion and discussion}

The variation patterns of the extreme precipitation and the
contributions of climate change and urbanization in the UAs over the LP
from 1979 to 2018 are analyzed in this study. The results reveal that
the precipitation intensity during the presummer season has decreased in
the southeastern LP and increased in the northwestern LP, and the
variation trends are much more significant in the urban areas than in
the rural areas. Specifically, the urbanization can aggravate the
decreasing trends of extreme precipitation in Taiyuan and Xi'an UAs
while enhance the increasing trends in Luoyang, Hohhot and Xining UAs.
The number of light rain days is very sensitive to the urbanization,
which decreases in almost all the cities. Although Xining UA has a lower
level of urbanization compared to the other UAs over the LP, the
urbanization effect is also detected in the wetting trend.

The results also display that the climate change is the dominant factor
for the urban precipitation trends, but the urbanization exhibits
different influences in different periods. In the first twenty years,
the urbanization contribution in the urban precipitation is relatively
weak, and the climate change is the primary factor. In the last twenty
years, the urbanization contribution increases dramatically, while the
contribution of climate change is much weaker than that in the first
stage. The urbanization tends to exert negative impacts on the extreme
precipitation indices in Xi'an and Taiyuan UAs and positive effects in
Luoyang, Hohhot and Xining UAs. In addition, the stronger
intensification of extreme precipitation in the urban areas may be
closely connected with the faster increase of surface temperature and
aerosol particles. To further examine the physical processes of the
urbanization effect on the extreme events in the UAs over different
geographical locations and under different climate backgrounds across
the LP, the dynamic experiments based on the Weather Research and
Forecasting model will be performed in our forthcoming studies.

The climate environment of the UAs over the LP is very sensitive and
fragile. Due to the considerable trends of extreme precipitation caused
by the climate change and amplified by the urbanization, urban
disasters, such as urban water shortage and urban waterlogging, are
expected to be further strengthened. Scientific urbanization planning
and construction should attract attention to alleviate the urbanization
consequences, particularly in large cities.

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